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<li><a class="reference internal" href="#">Model Complexity Influence</a></li>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-applications-plot-model-complexity-influence-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
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<div class="sphx-glr-example-title section" id="model-complexity-influence">
<span id="sphx-glr-auto-examples-applications-plot-model-complexity-influence-py"></span><h1>Model Complexity Influence<a class="headerlink" href="#model-complexity-influence" title="Permalink to this headline">¶</a></h1>
<p>Demonstrate how model complexity influences both prediction accuracy and
computational performance.</p>
<p>The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for
regression (resp. classification).</p>
<p>For each class of models we make the model complexity vary through the choice
of relevant model parameters and measure the influence on both computational
performance (latency) and predictive power (MSE or Hamming Loss).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="c1"># Author: Eustache Diemert &lt;eustache@diemert.fr&gt;</span>
<span class="c1"># License: BSD 3 clause</span>

<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1.parasite_axes</span> <span class="kn">import</span> <span class="n">host_subplot</span>
<span class="kn">from</span> <span class="nn">mpl_toolkits.axisartist.axislines</span> <span class="kn">import</span> <span class="n">Axes</span>
<span class="kn">from</span> <span class="nn">scipy.sparse.csr</span> <span class="kn">import</span> <span class="n">csr_matrix</span>

<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">shuffle</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">NuSVR</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingRegressor</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">hamming_loss</span>

<span class="c1"># #############################################################################</span>
<span class="c1"># Routines</span>


<span class="c1"># Initialize random generator</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">generate_data</span><span class="p">(</span><span class="n">case</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Generate regression/classification data.&quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">case</span> <span class="o">==</span> <span class="s1">&#39;regression&#39;</span><span class="p">:</span>
        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_boston</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="k">elif</span> <span class="n">case</span> <span class="o">==</span> <span class="s1">&#39;classification&#39;</span><span class="p">:</span>
        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">fetch_20newsgroups_vectorized</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">,</span>
                                                      <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    <span class="n">offset</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="n">offset</span><span class="p">],</span> <span class="n">y</span><span class="p">[:</span><span class="n">offset</span><span class="p">]</span>
    <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">offset</span><span class="p">:],</span> <span class="n">y</span><span class="p">[</span><span class="n">offset</span><span class="p">:]</span>
    <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
        <span class="n">X_train</span> <span class="o">=</span> <span class="n">csr_matrix</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
        <span class="n">X_test</span> <span class="o">=</span> <span class="n">csr_matrix</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
        <span class="n">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="n">y_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span>
    <span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
    <span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;X_train&#39;</span><span class="p">:</span> <span class="n">X_train</span><span class="p">,</span> <span class="s1">&#39;X_test&#39;</span><span class="p">:</span> <span class="n">X_test</span><span class="p">,</span> <span class="s1">&#39;y_train&#39;</span><span class="p">:</span> <span class="n">y_train</span><span class="p">,</span>
            <span class="s1">&#39;y_test&#39;</span><span class="p">:</span> <span class="n">y_test</span><span class="p">}</span>
    <span class="k">return</span> <span class="n">data</span>


<span class="k">def</span> <span class="nf">benchmark_influence</span><span class="p">(</span><span class="n">conf</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Benchmark influence of :changing_param: on both MSE and latency.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">prediction_times</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">prediction_powers</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">complexities</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">param_value</span> <span class="ow">in</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;changing_param_values&#39;</span><span class="p">]:</span>
        <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;tuned_params&#39;</span><span class="p">][</span><span class="n">conf</span><span class="p">[</span><span class="s1">&#39;changing_param&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">param_value</span>
        <span class="n">estimator</span> <span class="o">=</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;estimator&#39;</span><span class="p">](</span><span class="o">**</span><span class="n">conf</span><span class="p">[</span><span class="s1">&#39;tuned_params&#39;</span><span class="p">])</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Benchmarking </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">estimator</span><span class="p">)</span>
        <span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">conf</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;X_train&#39;</span><span class="p">],</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;y_train&#39;</span><span class="p">])</span>
        <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;postfit_hook&#39;</span><span class="p">](</span><span class="n">estimator</span><span class="p">)</span>
        <span class="n">complexity</span> <span class="o">=</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;complexity_computer&#39;</span><span class="p">](</span><span class="n">estimator</span><span class="p">)</span>
        <span class="n">complexities</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">complexity</span><span class="p">)</span>
        <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">conf</span><span class="p">[</span><span class="s1">&#39;n_samples&#39;</span><span class="p">]):</span>
            <span class="n">y_pred</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">conf</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;X_test&#39;</span><span class="p">])</span>
        <span class="n">elapsed_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">conf</span><span class="p">[</span><span class="s1">&#39;n_samples&#39;</span><span class="p">])</span>
        <span class="n">prediction_times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">elapsed_time</span><span class="p">)</span>
        <span class="n">pred_score</span> <span class="o">=</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;prediction_performance_computer&#39;</span><span class="p">](</span>
            <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;y_test&#39;</span><span class="p">],</span> <span class="n">y_pred</span><span class="p">)</span>
        <span class="n">prediction_powers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred_score</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Complexity: </span><span class="si">%d</span><span class="s2"> | </span><span class="si">%s</span><span class="s2">: </span><span class="si">%.4f</span><span class="s2"> | Pred. Time: </span><span class="si">%f</span><span class="s2">s</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span>
            <span class="n">complexity</span><span class="p">,</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;prediction_performance_label&#39;</span><span class="p">],</span> <span class="n">pred_score</span><span class="p">,</span>
            <span class="n">elapsed_time</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">prediction_powers</span><span class="p">,</span> <span class="n">prediction_times</span><span class="p">,</span> <span class="n">complexities</span>


<span class="k">def</span> <span class="nf">plot_influence</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="n">mse_values</span><span class="p">,</span> <span class="n">prediction_times</span><span class="p">,</span> <span class="n">complexities</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Plot influence of model complexity on both accuracy and latency.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
    <span class="n">host</span> <span class="o">=</span> <span class="n">host_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">axes_class</span><span class="o">=</span><span class="n">Axes</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
    <span class="n">par1</span> <span class="o">=</span> <span class="n">host</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
    <span class="n">host</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Model Complexity (</span><span class="si">%s</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;complexity_label&#39;</span><span class="p">])</span>
    <span class="n">y1_label</span> <span class="o">=</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;prediction_performance_label&#39;</span><span class="p">]</span>
    <span class="n">y2_label</span> <span class="o">=</span> <span class="s2">&quot;Time (s)&quot;</span>
    <span class="n">host</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">y1_label</span><span class="p">)</span>
    <span class="n">par1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">y2_label</span><span class="p">)</span>
    <span class="n">p1</span><span class="p">,</span> <span class="o">=</span> <span class="n">host</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">complexities</span><span class="p">,</span> <span class="n">mse_values</span><span class="p">,</span> <span class="s1">&#39;b-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;prediction error&quot;</span><span class="p">)</span>
    <span class="n">p2</span><span class="p">,</span> <span class="o">=</span> <span class="n">par1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">complexities</span><span class="p">,</span> <span class="n">prediction_times</span><span class="p">,</span> <span class="s1">&#39;r-&#39;</span><span class="p">,</span>
                    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;latency&quot;</span><span class="p">)</span>
    <span class="n">host</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">)</span>
    <span class="n">host</span><span class="o">.</span><span class="n">axis</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="n">p1</span><span class="o">.</span><span class="n">get_color</span><span class="p">())</span>
    <span class="n">par1</span><span class="o">.</span><span class="n">axis</span><span class="p">[</span><span class="s2">&quot;right&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="n">p2</span><span class="o">.</span><span class="n">get_color</span><span class="p">())</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Influence of Model Complexity - </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">conf</span><span class="p">[</span><span class="s1">&#39;estimator&#39;</span><span class="p">]</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>


<span class="k">def</span> <span class="nf">_count_nonzero_coefficients</span><span class="p">(</span><span class="n">estimator</span><span class="p">):</span>
    <span class="n">a</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>

<span class="c1"># #############################################################################</span>
<span class="c1"># Main code</span>
<span class="n">regression_data</span> <span class="o">=</span> <span class="n">generate_data</span><span class="p">(</span><span class="s1">&#39;regression&#39;</span><span class="p">)</span>
<span class="n">classification_data</span> <span class="o">=</span> <span class="n">generate_data</span><span class="p">(</span><span class="s1">&#39;classification&#39;</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">configurations</span> <span class="o">=</span> <span class="p">[</span>
    <span class="p">{</span><span class="s1">&#39;estimator&#39;</span><span class="p">:</span> <span class="n">SGDClassifier</span><span class="p">,</span>
     <span class="s1">&#39;tuned_params&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;penalty&#39;</span><span class="p">:</span> <span class="s1">&#39;elasticnet&#39;</span><span class="p">,</span> <span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="mf">0.001</span><span class="p">,</span> <span class="s1">&#39;loss&#39;</span><span class="p">:</span>
                      <span class="s1">&#39;modified_huber&#39;</span><span class="p">,</span> <span class="s1">&#39;fit_intercept&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;tol&#39;</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">},</span>
     <span class="s1">&#39;changing_param&#39;</span><span class="p">:</span> <span class="s1">&#39;l1_ratio&#39;</span><span class="p">,</span>
     <span class="s1">&#39;changing_param_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">],</span>
     <span class="s1">&#39;complexity_label&#39;</span><span class="p">:</span> <span class="s1">&#39;non_zero coefficients&#39;</span><span class="p">,</span>
     <span class="s1">&#39;complexity_computer&#39;</span><span class="p">:</span> <span class="n">_count_nonzero_coefficients</span><span class="p">,</span>
     <span class="s1">&#39;prediction_performance_computer&#39;</span><span class="p">:</span> <span class="n">hamming_loss</span><span class="p">,</span>
     <span class="s1">&#39;prediction_performance_label&#39;</span><span class="p">:</span> <span class="s1">&#39;Hamming Loss (Misclassification Ratio)&#39;</span><span class="p">,</span>
     <span class="s1">&#39;postfit_hook&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">sparsify</span><span class="p">(),</span>
     <span class="s1">&#39;data&#39;</span><span class="p">:</span> <span class="n">classification_data</span><span class="p">,</span>
     <span class="s1">&#39;n_samples&#39;</span><span class="p">:</span> <span class="mi">30</span><span class="p">},</span>
    <span class="p">{</span><span class="s1">&#39;estimator&#39;</span><span class="p">:</span> <span class="n">NuSVR</span><span class="p">,</span>
     <span class="s1">&#39;tuned_params&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="mf">1e3</span><span class="p">,</span> <span class="s1">&#39;gamma&#39;</span><span class="p">:</span> <span class="mi">2</span> <span class="o">**</span> <span class="o">-</span><span class="mi">15</span><span class="p">},</span>
     <span class="s1">&#39;changing_param&#39;</span><span class="p">:</span> <span class="s1">&#39;nu&#39;</span><span class="p">,</span>
     <span class="s1">&#39;changing_param_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">],</span>
     <span class="s1">&#39;complexity_label&#39;</span><span class="p">:</span> <span class="s1">&#39;n_support_vectors&#39;</span><span class="p">,</span>
     <span class="s1">&#39;complexity_computer&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">),</span>
     <span class="s1">&#39;data&#39;</span><span class="p">:</span> <span class="n">regression_data</span><span class="p">,</span>
     <span class="s1">&#39;postfit_hook&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span>
     <span class="s1">&#39;prediction_performance_computer&#39;</span><span class="p">:</span> <span class="n">mean_squared_error</span><span class="p">,</span>
     <span class="s1">&#39;prediction_performance_label&#39;</span><span class="p">:</span> <span class="s1">&#39;MSE&#39;</span><span class="p">,</span>
     <span class="s1">&#39;n_samples&#39;</span><span class="p">:</span> <span class="mi">30</span><span class="p">},</span>
    <span class="p">{</span><span class="s1">&#39;estimator&#39;</span><span class="p">:</span> <span class="n">GradientBoostingRegressor</span><span class="p">,</span>
     <span class="s1">&#39;tuned_params&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;loss&#39;</span><span class="p">:</span> <span class="s1">&#39;ls&#39;</span><span class="p">},</span>
     <span class="s1">&#39;changing_param&#39;</span><span class="p">:</span> <span class="s1">&#39;n_estimators&#39;</span><span class="p">,</span>
     <span class="s1">&#39;changing_param_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">500</span><span class="p">],</span>
     <span class="s1">&#39;complexity_label&#39;</span><span class="p">:</span> <span class="s1">&#39;n_trees&#39;</span><span class="p">,</span>
     <span class="s1">&#39;complexity_computer&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">n_estimators</span><span class="p">,</span>
     <span class="s1">&#39;data&#39;</span><span class="p">:</span> <span class="n">regression_data</span><span class="p">,</span>
     <span class="s1">&#39;postfit_hook&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span>
     <span class="s1">&#39;prediction_performance_computer&#39;</span><span class="p">:</span> <span class="n">mean_squared_error</span><span class="p">,</span>
     <span class="s1">&#39;prediction_performance_label&#39;</span><span class="p">:</span> <span class="s1">&#39;MSE&#39;</span><span class="p">,</span>
     <span class="s1">&#39;n_samples&#39;</span><span class="p">:</span> <span class="mi">30</span><span class="p">},</span>
<span class="p">]</span>
<span class="k">for</span> <span class="n">conf</span> <span class="ow">in</span> <span class="n">configurations</span><span class="p">:</span>
    <span class="n">prediction_performances</span><span class="p">,</span> <span class="n">prediction_times</span><span class="p">,</span> <span class="n">complexities</span> <span class="o">=</span> \
        <span class="n">benchmark_influence</span><span class="p">(</span><span class="n">conf</span><span class="p">)</span>
    <span class="n">plot_influence</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="n">prediction_performances</span><span class="p">,</span> <span class="n">prediction_times</span><span class="p">,</span>
                   <span class="n">complexities</span><span class="p">)</span>
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